Here I investigate the amount of money that British MPs claimed in different expense each categories.
After the expenses scandal in 2010, MPs were forced to submit their expenses claims and the data would be made publicly available.
I thought it would be interesting to see what the MPs spent our money on!
I wrangled the data with Pandas and visualised it with Plotly.
You can fork it at my Github.

In this analysis I investigate which MPs had consistently high and low expense claim amounts.
The total amount claimed was highest in 2013 and 2014.
There were 2 MPs who claimed around £500k each in just two years.
I wrangled the data with Pandas and visualised it with Plotly.
You can fork it at my Github.

In this analysis I get data in different formats (.csv and .xslx) from the ONS and manipulate it ready for analysis.

The first part of this analysis deals with how to wrangle and manipulate the data into a format which can be easily analysed.
I had to overcome a lot of problems when loading the data in from different sources (.csv, .xls etc.),
and the solutions I found will hopefully be applicable to any analyses which you do! Please take and reuse my code as you see fit – you can fork it at my Github.

In this analysis I use the data which I cleaned here to create a multiple linear regression model.
I firstly trained a regression model on each variable against the average deprivation score using the Sklearn library and used this information to decide which variables to consider for the multiple regression.
Next, I created a function which runs a multiple regression on every combination of variables, and selects the best model using criteria I developed.
I analysed the data with Sklearn, SciPy and Statsmodels.
You can fork it at my Github.